neural network

Introduction to Resnet or Residual Network

Over the last few years, there have been a series of breakthroughs in the field of Computer Vision.Especially with the introduction of deep Convolutional neural networks, we are getting state of the art results on problems such as image classification and image recognition. So, over the years, researchers tend to make deeper neural networks(adding more layers)

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vanishing gradient problem

The Vanishing Gradient Problem

What is the Vanishing Gradient? The vanishing gradient is a phenomenon in which the gradients used to update the weights in neural networks become very small, tending closely towards zero. This becomes a problem when dealing with deep networks that have multiple hidden layers. In backpropagation, we pass the gradients of the loss function backward from

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Activation Functions in Neural Networks Explained

Activation functions play a crucial role in neural networks by determining whether a neuron should be activated or not. They introduce non-linearity, allowing networks to learn complex patterns. Without activation functions, a neural network would behave like a simple linear model, limiting its ability to solve real-world problems. In this article, we’ll explore different types

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neural network

3 Things to know before Deep diving into Neural Networks

With Artificial Intelligence and its technology taking over the world, the demand and the interest around the study peaks an upward graph. The hype surrounding AI boils down to its most basic cornerstone – neural networks. If you are a tech fanatic, and if you have managed to tweak your attention towards deep-diving into neural

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